Aims and Scope

Aims

The Journal of Healthcare Analytics and Informatics (JHAI) publishes research on how data, analytics and computing can improve clinical decisions, health services and patient outcomes. It takes original research, systematic and methodological reviews, and short communications.

Papers are judged on originality, sound method, reproducibility and a clear account of what the work would change in care. Studies that draw on computer science, biostatistics, clinical research and public health together are welcome, as is careful work within a single field.

Scope

The scope covers three subject areas. The lists below give examples and are not exhaustive; a paper outside them but inside one of the three areas will be considered.

Clinical analytics and diagnostic AI

  • Clinical decision support, diagnosis, prognosis and risk prediction
  • Medical imaging, radiology and digital pathology AI
  • Early warning and deterioration detection in hospital wards
  • Physiological signals, wearables and cuffless monitoring
  • Precision medicine, genomic and multi-omics informatics
  • Clinical natural language processing of notes and reports

Health data, systems and services

  • Electronic health record analytics, interoperability (HL7, FHIR) and clinical terminologies
  • Hospital operations, patient flow and digital twins of care pathways
  • Remote patient monitoring, telemedicine and digital therapeutics
  • Epidemiological modelling, public health surveillance and population health
  • Edge and federated computing for health data

Safe, fair and trustworthy clinical AI

  • Explainability, calibration and uncertainty in clinical models
  • Fairness, bias and drift monitoring after deployment
  • Human oversight, override and safety envelopes for clinical AI
  • Privacy, security, regulation and governance of health data and AI

Examples of papers in scope

  • A systematic review of explainable AI for electronic health records and clinical time series
  • Multimodal fusion of wearable and facial video signals for cuffless blood pressure estimation
  • Channel pruning guided by electrophysiology for rare arrhythmia detection on edge devices
  • Prediction-level safety envelopes that combine uncertainty, fairness drift and human override
  • Digital twins of escalation pathways for predicting failure to rescue on general wards
  • Measuring how quickly trust in a clinical AI model decays under data and fairness drift

Papers usually declined at the desk

Every submission is checked for fit, method and originality before it goes to reviewers. The following are normally declined at that stage:

  • Studies using identifiable patient data without ethics approval or a stated legal basis
  • Routine application of an existing model to a public dataset with no clinical or methodological contribution
  • Prediction models reported without calibration, a held-out test set or any measure of uncertainty
  • Product descriptions or feasibility notes without a research contribution
  • Literature summaries that offer no methodological synthesis

If you are unsure whether a paper fits, send a short pre-submission enquiry through the Contact page.

Article types

  • Original Research Article. A full-length clinical, computational or methodological study with new findings.
  • Systematic or Methodological Review. A structured synthesis following PRISMA, bibliometric or a comparable method.
  • Short Communication. A concise report of a focused finding or a brief methodological note.
  • Editorial. By invitation only, written by the editors or invited contributors.

Length and format for each type are given in the Author Guidelines.